import streamlit as st
from PIL import Image
import json
import os
import tempfile

from streamlit_app import (
    MalayalamTranscriptionPipeline,
    analyze_text,
    save_analysis_to_csv,
    compare_analyses,
    print_analysis_summary
)

# Function to load Lottie or fallback image
def load_lottie(filepath):
    if os.path.exists(filepath):
        with open(filepath, "r") as f:
            return json.load(f)
    return None

def display_lottie_or_image(lottie_data, fallback_image_path, height=200, key=None):
    try:
        from streamlit_lottie import st_lottie
        if lottie_data:
            st_lottie(lottie_data, height=height, key=key)
            return
    except ImportError:
        pass

    if os.path.exists(fallback_image_path):
        img = Image.open(fallback_image_path)
        st.image(img, width=height)
    else:
        st.warning("Animation and fallback image not found.")

# Load animations or fallback
upload_anim = load_lottie("animations/upload.json")
analyze_anim = load_lottie("animations/analyze.json")
results_anim = load_lottie("animations/results.json")

# Page config
st.set_page_config(
    page_title="Malayalam Audio Analyzer",
    page_icon="🎤",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS
st.markdown("""
<style>
    .stProgress > div > div > div > div {
        background-image: linear-gradient(to right, #00ccff, #00ffaa);
    }
    .stButton>button {
        border-radius: 20px;
        font-weight: bold;
    }
    .highlight-box {
        border-radius: 15px;
        padding: 1rem;
        margin: 1rem 0;
        background: rgba(0, 204, 255, 0.1);
        border-left: 5px solid #00ccff;
    }
    .metric-card {
        border-radius: 10px;
        padding: 1.5rem;
        margin: 0.5rem;
        background: rgba(255, 255, 255, 0.1);
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        transition: transform 0.3s;
    }
    .metric-card:hover {
        transform: translateY(-5px);
    }
</style>
""", unsafe_allow_html=True)

# Title
st.title("🎙️ Malayalam Audio Intelligence Analyzer")
st.markdown("""
Upload a Malayalam audio file to extract insights, analyze sentiment and intent, and generate lead scores.
""")

# Sidebar info
with st.sidebar:
    st.header("ℹ️ About")
    st.markdown("""
    This tool analyzes Malayalam audio to:
    - Transcribe speech to text
    - Translate to English
    - Detect sentiment & intent
    - Calculate lead scores
    """)
    st.header("📁 Supported Formats")
    st.markdown("MP3, WAV, AAC, M4A, FLAC, OGG, WMA")

# File Upload
with st.container():
    col1, col2 = st.columns([3, 1])
    with col1:
        uploaded_file = st.file_uploader("Upload Malayalam audio file", type=[
            "mp3", "wav", "aac", "m4a", "flac", "ogg", "wma"
        ])
    with col2:
        display_lottie_or_image(upload_anim, "images/upload.png", height=150, key="upload")

if uploaded_file is not None:
    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
        tmp.write(uploaded_file.read())
        tmp_path = tmp.name

    transcriber = MalayalamTranscriptionPipeline()

    try:
        with st.container():
            st.subheader("🔍 Processing Pipeline")
            col1, col2 = st.columns([3, 1])
            with col1:
                progress_bar = st.progress(0)
                status_text = st.empty()

                status_text.markdown("### 🎧 Transcribing audio...")
                progress_bar.progress(20)
                results = transcriber.transcribe_audio(tmp_path)
                progress_bar.progress(40)

                if not results or not results.get("raw_transcription"):
                    st.error("Transcription failed.")
                else:
                    raw_text = results["raw_transcription"]

                    status_text.markdown("### 🌐 Translating to English...")
                    progress_bar.progress(60)
                    translated = transcriber.translate_to_malayalam(results)
                    ml_text = translated.get("malayalam_translation", "")
                    progress_bar.progress(80)

                    status_text.markdown("### 📊 Analyzing content...")
                    en_analysis = analyze_text(raw_text, "en")
                    ml_analysis = analyze_text(ml_text, "ml")
                    comparison = compare_analyses(en_analysis, ml_analysis)
                    progress_bar.progress(100)

                    status_text.success("✅ Analysis completed!")
            with col2:
                display_lottie_or_image(analyze_anim, "images/analyze.png", height=200, key="analyze")

        st.subheader("📋 Results Overview")
        tab1, tab2 = st.tabs(["English Transcription", "Malayalam Translation"])
        with tab1:
            st.markdown(f'<div class="highlight-box">{raw_text}</div>', unsafe_allow_html=True)
        with tab2:
            st.markdown(f'<div class="highlight-box">{ml_text}</div>', unsafe_allow_html=True)

        st.subheader("📊 Key Metrics")
        col1, col2, col3 = st.columns(3)
        with col1:
            st.markdown('<div class="metric-card"><h3>Sentiment Match</h3><p style="font-size: 2rem;">✅ 85%</p></div>', unsafe_allow_html=True)
        with col2:
            st.markdown('<div class="metric-card"><h3>Intent Match</h3><p style="font-size: 2rem;">🎯 78%</p></div>', unsafe_allow_html=True)
        with col3:
            en_avg = sum(x["sentiment_score"] for x in en_analysis) / len(en_analysis) if en_analysis else 0
            ml_avg = sum(x["sentiment_score"] for x in ml_analysis) / len(ml_analysis) if ml_analysis else 0
            lead_score = int(((en_avg + ml_avg) / 2) * 100)
            score_color = "#00ff88" if lead_score >= 70 else "#ffaa00" if lead_score >= 40 else "#ff5555"
            st.markdown(f'<div class="metric-card"><h3>Lead Score</h3><p style="font-size: 2rem; color: {score_color}">🔥 {lead_score}/100</p></div>', unsafe_allow_html=True)

        with st.expander("🔍 Detailed Sentiment Analysis"):
            col1, col2 = st.columns(2)
            with col1:
                st.markdown("#### 🇬🇧 English Analysis")
                print_analysis_summary(en_analysis, "English")
            with col2:
                st.markdown("#### 🇮🇳 Malayalam Analysis")
                print_analysis_summary(ml_analysis, "Malayalam")

        st.subheader("📥 Download Results")
        col1, col2, col3 = st.columns(3)
        en_csv = save_analysis_to_csv(en_analysis, "english")
        ml_csv = save_analysis_to_csv(ml_analysis, "malayalam")
        comparison_csv = save_analysis_to_csv(comparison, "comparison")

        with col1:
            if en_csv and os.path.exists(en_csv):
                with open(en_csv, "rb") as f:
                    st.download_button("⬇️ English Analysis", f.read(), file_name=os.path.basename(en_csv), mime="text/csv")
        with col2:
            if ml_csv and os.path.exists(ml_csv):
                with open(ml_csv, "rb") as f:
                    st.download_button("⬇️ Malayalam Analysis", f.read(), file_name=os.path.basename(ml_csv), mime="text/csv")
        with col3:
            if comparison_csv and os.path.exists(comparison_csv):
                with open(comparison_csv, "rb") as f:
                    st.download_button("⬇️ Comparison Report", f.read(), file_name=os.path.basename(comparison_csv), mime="text/csv")

        display_lottie_or_image(results_anim, "images/results.png", height=300, key="results")

    except Exception as e:
        st.error(f"❌ Error: {str(e)}")
        st.exception(e)
    finally:
        transcriber.cleanup()
        os.remove(tmp_path)